Predicting online user behaviour using deep learning algorithms
This work addresses predicting user behavior for e-commerce platforms, but it is incremental as it applies existing deep learning methods to a specific domain.
The authors tackled the problem of predicting user buying intentions on an e-commerce website by comparing traditional machine learning with deep learning methods, finding that Deep Belief Networks and Stacked Denoising Auto-Encoders achieved substantial improvements in handling high-dimensional data and class imbalance.
We propose a robust classifier to predict buying intentions based on user behaviour within a large e-commerce website. In this work we compare traditional machine learning techniques with the most advanced deep learning approaches. We show that both Deep Belief Networks and Stacked Denoising auto-Encoders achieved a substantial improvement by extracting features from high dimensional data during the pre-train phase. They prove also to be more convenient to deal with severe class imbalance.